Databases are used to track a large amount of data collected during the regular course of business operations and events. Businesses typically store data regarding sales and sales projections, profit, inventory, payroll, human resources, and much more. Sports leagues create and maintain large data warehouses to record scores, standings, and statistics for every team and every player. As the amount of data increases, there is an increasing challenge to extract meaning from the data. For example, it becomes more difficult to identify hierarchical structures, logic patterns, and complicated relationships hidden amongst the data.
Graphical data visualizations can be effective to convey information and to enable a person to analyze the data. In particular, data visualizations can aid in human understanding of relationships and patterns in the data. Many people construct data visualizations manually, which is both difficult and time consuming. Data visualization applications assist in visualizing data, but many do not support visualizing relationships. Some data visualization applications can create simple node-link diagrams, are not designed to present complex data relationships, such as manager reporting structures, product categories, a social network, family relationships, paper citations, a programming class hierarchy, or hyperlinks. Furthermore, data visualizations with relationships are particularly difficult to present when the amount of data increases.